Conceptually, we argue that our approach, while novel, is not entirely separate from existing methods. Our reliance on lower bounds make the method an instance of variational inference [1]}, [2]}, and we maximize the bounds using Monte Carlo EM [3]}. We rely on a la as our choice of variational distribution, which has also been explored by [4]}. Finally, we note that our linearization trick (REF ) bares great similarities to classic extended Kalman filtering [5]}.
